Statistical Methods for the Social Sciences

Hardcover | December 28, 2007

byAlan Agresti, Barbara Finlay

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The book presents an introduction to statistical methods for students majoring in social science disciplines.  No previous knowledge of statistics is assumed, and mathematical background is assumed to be minimal (lowest-level high-school algebra).


The book contains sufficient material for a two-semester sequence of courses.  Such sequences are commonly required of social science graduate students in sociology, political science, and psychology. Students in geography, anthropology, journalism, and speech also are sometimes required to take at least one statistics course.

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The book presents an introduction to statistical methods for students majoring in social science disciplines.  No previous knowledge of statistics is assumed, and mathematical background is assumed to be minimal (lowest-level high-school algebra).  The book contains sufficient material for a two-semester sequence of courses.  Such seq...

Alan Agresti is Distinguished Professor in the Department of Statistics at the University of Florida. He has been teaching statistics there for 30 years, including the development of three courses in statistical methods for social science students and three courses in categorical data analysis. He is author of over 100 refereed article...

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Format:HardcoverDimensions:624 pages, 10 × 8.1 × 1.1 inPublished:December 28, 2007Publisher:Pearson EducationLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0130272957

ISBN - 13:9780130272959

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Extra Content

Table of Contents


    1.1 Introduction to statistical methodology

    1.2 Descriptive statistics and inferential statistics

    1.3 The role of computers in statistics

    1.4 Chapter summary

2. Sampling and Measurement

    2.1 Variables and their measurement

    2.2 Randomization

    2.3 Sampling variability and potential bias

    2.4 other probability sampling methods *

    2.4 Chapter summary

3. Descriptive statistics

    3.1 Describing data with tables and graphs

    3.2 Describing the center of the data

    3.3 Describing variability of the data

    3.4 Measure of position

    3.5 Bivariate descriptive statistics

    3.6 Sample statistics and population parameters

    3.7 Chapter summary

4. Probability Distributions

    4.1 Introduction to probability

    4.2 Probablitity distributions for discrete and  continuous variables

    4.3 The normal probability distribution

    4.4 Sampling distributions describe how statistics vary

    4.5 Sampling distributions of sample means

    4.6 Review: Probability, sample data, and sampling distributions

    4.7 Chapter summary

5. Statistical inference: estimation

    5.1 Point and interval estimation

    5.2 Confidence interval for a proportion

    5.3 Confidence interval for a mean

    5.4 Choice of sample size

    5.5 Confidence intervals for median and other parameters*

    5.6 Chapter summary

 6. Statistical Inference: Significance Tests

    6.1 Steps of a significance test

    6.2 Significance test for a eman

    6.3 Significance test for a proportion

    6.4 Decisions and types of errors in tests

    6.5 Limitations of significance tests

    6.6 Calculating P (Type II error)*

    6.7 Small-sample test for a proportion: the binomial distribution*

    6.8 Chapter summary

7. Comparison of Two Groups

    7.1 Preliminaries for comparing groups

    7.2 Categorical data: comparing two proportions

    7.3 Quantitative data: comparing two means

    7.4 Comparing means with dependent samples

    7.5 Other methods for comparing means*

    7.6 Other methods for comparing proportions*

    7.7 Nonparametric statistics for comparing groups

    7.8 Chapter summary

8. Analyzing Association between Categorical Variables

    8.1 Contingency Tables

    8.2 Chi-squared test of independence

    8.3 Residuals: Detecting the pattern of association

    8.4 Measuring association in contingency tables

    8.5 Association between ordinal variables*

    8.6 Inference for ordinal associations*

    8.7 Chapter summary

9. Linear Regression and Correlation

    9.1 Linear relationships

    9.2 Least squares prediction equation

    9.3 The linear regression model

    9.4 Measuring linear association - the correlation

    9.5 Inference for the slope and correlation

    9.6 Model assumptions and violations

    9.7 Chapter summary

10. Introduction to multivariate Relationships

    10.1 Association and causality

    10.2 Controlling for other variables

    10.3 Types of multivariate relationships

    10.4 Inferenential issus in statistical control

    10.5 Chapter summary

11. Multiple Regression and Correlation

    11.1 Multiple regression model

    11.2 Example with multiple regression computer output

    11.3 Multiple correlation and R-squared

    11.4 Inference for multiple regression and coefficients

    11.5 Interaction between predictors in their effects

    11.6 Comparing regression models

    11.7 Partial correlation*

    11.8 Standardized regression coefficients*

    11.9 Chapter summary

12. Comparing groups: Analysis of Variance (ANOVA) methods

    12.1 Comparing several means: One way analysis of variance

    12.2 Multiple comparisons of means

    12.3 Performing ANOVA by regression modeling

    12.4 Two-way analysis of variance

    12.5 Two way ANOVA and regression

    12.6 Repeated measures analysis of variance*

    12.7 Two-way ANOVA with repeated measures on one factor*

    12.8 Effects of violations of ANOVA assumptions

    12.9 Chapter summary

13. Combining regression and ANOVA: Quantitative and Categorical Predictors

    13.1 Comparing means and comparing regression lines

    13.2 Regression with quantitative and categorical predictors

    13.3 Permitting interaction between quantitative and categorical predictors

    13.4 Inference for regression with quantitative and categorical predictors

    13.5 Adjusted means*

    13.6 Chapter summary

14. Model Building with Multiple Regression

    14.1 Model selection procedures

    14.2 Regression diagnostics

    14.3 Effects of multicollinearity

    14.4 Generalized linear models

    14.5 Nonlinearity: polynomial regression

    14.6 Exponential regression and log transforms*

    14.7 Chapter summary

15. Logistic Regression: Modeling Categorical Responses

    15.1 Logistic regression

    15.2 Multiple logistic regression

    15.3 Inference for logistic regression models

    15.4 Logistic regression models for ordinal variables*

    15.5 Logistic models for nominal responses*

    15.6 Loglinear models for categorical variables*

    15.7 Model goodness of fit tests for contingency tables*

    15.9 Chapter summary

16. Introduction to Advanced Topics

    16.1 Longitudinal data analysis*

    16.2 Multilevel (hierarchical) models*

    16.3 Event history analysis*

    16.4 Path analysis*

    16.5 Factor analysis*

    16.6 Structural equation models*

    16.7 Markov chains*

Appendix: SAS and SPSS for Statistical Analyses


Answers to selected odd-numbered problems




Editorial Reviews

"This text is readable, understandable, and well-organized. It provides good examples with SPSS output." (Robert Wilson, University of Delaware).   "Overall, [Agresti/ Finlay] is a good book for introductory statistics that targets general covers most topics you want to cover and allows the instructor to choose which topics to include." (Youqin Huang, State University of New York, Albany)   "I originally started using the Agresti/ Finlay book based on its reputation as "the class of the market", in terms of being unfailingly statistically correct and having a "modern" perspective. By "modern", I mean that it is model rather than test oriented, that it gives heavy emphasis to confidence intervals and p-values rather than using arbitrary levels of significance, and that it eschews computational formulae. It has met those expectations..." (Michael Lacey, Colorado State University)   "..the book has been a good and helpful resource for me in preparing the class notes and assigning homework qustions. The main concepts to be understood by students are sampling distribution, confidence interval, p-value, linear regression. The book helps in this..." (Arne Bathke, University of Kentucky)